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  COMMERCIAL SECRET MANAGEMENT IN TERMS OF SHADOW DIGITAL ECONOMY

  The article explores trade secret protection management in the context of the shadow digital economy (SDE). The study aims to identify threats to trade secrets associated with SDE and propose effective protection methods. It emphasizes that the economic value of trade secrets lies in their confidentiality, making them vulnerable to data leaks, ... The article explores trade secret protection management in the context of the shadow digital economy (SDE). The study aims to identify threats to trade secrets associated with SDE and propose effective protection methods. It emphasizes that the economic value of trade secrets lies in their confidentiality, making them vulnerable to data leaks, cyberattacks, and other risks, particularly in the digital environment. The authors examine the nature of trade secret in terms of SDE spread. The paper presents methods of trade secret protection, including documentary, economic-organizational, and information-analytical types. Special attention is paid to the combined use of patent protection and encryption techniques. The study concludes with key findings, highlighting the need for enhanced cybersecurity, international cooperation to combat cyber espionage, and improved organizational measures. This work significantly contributes to the understanding of commercial secret management and offers valuable recommendations for businesses operating in the digital economy.

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  BUSINESS DEVELOPMENT OF DUBAI COMPANIES: A SYNTHESIZED FACTOR-ACTIVITY ANALYSIS

  Throughout the years, the notion of business development has been utilized in different dimensions. This study re-evaluates the concept via the perspective of strategic management. A synopsis of the correlation between developmental trajectory and strategic organizational change is presented. The focus is on developmental initiatives that ... Throughout the years, the notion of business development has been utilized in different dimensions. This study re-evaluates the concept via the perspective of strategic management. A synopsis of the correlation between developmental trajectory and strategic organizational change is presented. The focus is on developmental initiatives that substantially impact firm success. As known, developments are influenced by different envirnomental factors. The article examines the correlation between chosen prerequisites and particular actions inherent to business development. The objective is to ascertain the existence of relationships and evaluate their strength and importance through the examination of statistical hypotheses. This will establish improved prerequisites for formulating a robust corporate development strategy. The generalizations and conclusions are based on data and information gathered via a survey in the Emirate of Dubai, United Arab Emirates. The territorial concentration of the study is due to its compactness, development potential and stated local research interest.

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  APPLICATION OF MACHINE LEARNING ALGORITHMS IN PREDICTING CUSTOMER LOYALTY TOWARDS GROCERY RETAILERS

  Retailers strive for customer loyalty in the sense of repeat purchases, but also as a high proportion of purchases (compared to competitors) and willingness to recommend to other customers. This paper examines customer loyalty in the grocery sector as a three-dimensional construct and shows how machine learning techniques can be useful in its ... Retailers strive for customer loyalty in the sense of repeat purchases, but also as a high proportion of purchases (compared to competitors) and willingness to recommend to other customers. This paper examines customer loyalty in the grocery sector as a three-dimensional construct and shows how machine learning techniques can be useful in its study. Price characteristics of the retailer (price level, value for money, price dynamics, price communication and price dispersion) and non-price characteristics of the retailer (general product range, retailer's private label product range, store design and atmosphere, service level and location) are included in the model as predictor variables. Using the data collected through the primary research conducted in Croatia, 433 samples were divided into 10 independent predictor variables and one dependent variable (customer loyalty), a prediction was created using supervised machine learning classification algorithms. The Random Forest classifier proves to be the best choice overall, with ROC_AUC value of 0.790, a high accuracy of 0.915 and an F1 score of 0.954, reflecting both precision and responsiveness. The application of the SHapley Additive exPlanations analysis additionally enables the interpretation of the results, highlighting the influence of features on the accuracy of the prediction. The results indicate that price dynamics and service level are the most important features for the model predictions, followed by value for money and price communication.